TRAIL: An Efficient Industrial Ranking Framework via Interest Evolution and Target-Relevant Aggregation

Modeling long user behavior sequences is crucial for click-through rate (CTR) prediction, which requires efficiently capturing user interest evolution over long histories and target-relevant behaviors. Target-aware attention methods are efficient and directly model the relevance between target items and historical behaviors, but they mainly focus on matching the target item with individual behaviors, leaving interest evolution underexplored. Self-attentive models capture behavior interactions, but their sequence modeling is usually performed without conditioning on the target item and incurs quadratic computational cost. We propose TRAIL, an efficient framework for long sequence CTR prediction via interest evolution and target-relevant aggregation. TRAIL first applies the State-Compressed Gated DeltaNet (SC-GDN) to model interest-state evolution over the raw behavior sequence with linear complexity. It then uses Segmented Target Attention (STA) to construct target-relevant segment representations, reducing sequence length while preserving target-relevant behavioral signals. Over the compressed sequence, Global Stacked Target Attention (GSTA) further aggregates target-relevant information with low computational cost. Finally, a Multi-resolution Interest Fusion module integrates multi-level interest representations for CTR prediction. Experiments show that TRAIL achieves the best AUC and GAUC while using only about 15% of the FLOPs required by the full-attention model. Online A/B testing further yields +2.51% advertiser value and +2.36% revenue gains, demonstrating TRAIL's effectiveness and efficiency in industrial long sequence CTR modeling.